Left Ventricular Segmentation Research Articles

Image-based clustering and connected component labeling for rapid automated left and right ventricular endocardial volume extraction and segmentation in full cardiac cycle multi-frame MRI images of cardiac patients

A rapid method for left and right ventricular endocardial volume segmentation and clinical cardiac parameter calculation from MRI images of cardiac patients is presented. The clinical motivation is providing cardiologists a tool for assessing the cardiac function in a patient through the left ventricular endocardial volume's ejection fraction. A new method combining adapted fuzzy membership-based c-means pixel clustering and connected regions component labeling is used for automatic segmentation of the left and right ventricular endocardial volumes. This proposed pixel clustering with labeling approach avoids manual initialization or user intervention and does not require specifying the region of interest. This method fully automatically extracts the left and right ventricular endocardial volumes and avoids manual tracing on all MRI image frames in the complete cardiac cycle from systole to diastole. The average computational processing time per frame is 0.6s, making it much more efficient than deformable methods, which need several iterations for the evolution of the snake or contour. Accuracy of the automated method presented herein was validated against manual tracing-based extraction, performed with the guidance of cardiac experts, on several MRI frames. Dice coefficients between the proposed automatic versus manual traced ventricular endocardial volume segmentations were observed to be 0.9781 ± 0.0070 (for left ventricular endocardial volume) and 0.9819 ± 0.0058 (for right ventricular endocardial volume), and the Pearson correlation coefficients were observed to be 0.9655 ± 0.0206 (for left ventricular endocardial volume) and 0.9870 ± 0.0131 (for right ventricular endocardial volume). Graphical abstract The left ventricular endocardial volume segmentation methodology illustrated as a series of algorithms.

Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers.

Deep fully convolutional neural network (FCN) based architectures have shown great potential in medical image segmentation. However, such architectures usually have millions of parameters and inadequate number of training samples leading to over-fitting and poor generalization. In this paper, we present a novel DenseNet based FCN architecture for cardiac segmentation which is parameter and memory efficient. We propose a novel up-sampling path which incorporates long skip and short-cut connections to overcome the feature map explosion in conventional FCN based architectures. In order to process the input images at multiple scales and view points simultaneously, we propose to incorporate Inception module's parallel structures. We propose a novel dual loss function whose weighting scheme allows to combine advantages of cross-entropy and Dice loss leading to qualitative improvements in segmentation. We demonstrate computational efficacy of incorporating conventional computer vision techniques for region of interest detection in an end-to-end deep learning based segmentation framework. From the segmentation maps we extract clinically relevant cardiac parameters and hand-craft features which reflect the clinical diagnostic analysis and train an ensemble system for cardiac disease classification. We validate our proposed network architecture on three publicly available datasets, namely: (i) Automated Cardiac Diagnosis Challenge (ACDC-2017), (ii) Left Ventricular segmentation challenge (LV-2011), (iii) 2015 Kaggle Data Science Bowl cardiac challenge data. Our approach in ACDC-2017 challenge stood second place for segmentation and first place in automated cardiac disease diagnosis tasks with an accuracy of 100% on a limited testing set (n=50). In the LV-2011 challenge our approach attained 0.74 Jaccard index, which is so far the highest published result in fully automated algorithms. In the Kaggle challenge our approach for LV volume gave a Continuous Ranked Probability Score (CRPS) of 0.0127, which would have placed us tenth in the original challenge. Our approach combined both cardiac segmentation and disease diagnosis into a fully automated framework which is computationally efficient and hence has the potential to be incorporated in computer-aided diagnosis (CAD) tools for clinical application.

Deep Learning-based Method for Fully Automatic Quantification of Left Ventricle Function from Cine MR Images: A Multivendor, Multicenter Study.

Purpose To develop a deep learning-based method for fully automated quantification of left ventricular (LV) function from short-axis cine MR images and to evaluate its performance in a multivendor and multicenter setting. Materials and Methods This retrospective study included cine MRI data sets obtained from three major MRI vendors in four medical centers from 2008 to 2016. Three convolutional neural networks (CNNs) with the U-NET architecture were trained on data sets of increasing variability: (a) a single-vendor, single-center, homogeneous cohort of 100 patients (CNN1); (b) a single-vendor, multicenter, heterogeneous cohort of 200 patients (CNN2); and (c) a multivendor, multicenter, heterogeneous cohort of 400 patients (CNN3). All CNNs were tested on an independent multivendor, multicenter data set of 196 patients. CNN performance was evaluated with respect to the manual annotations from three experienced observers in terms of (a) LV detection accuracy, (b) LV segmentation accuracy, and (c) LV functional parameter accuracy. Automatic and manual results were compared with the paired Wilcoxon test, Pearson correlation, and Bland-Altman analysis. Results CNN3 achieved the highest performance on the independent testing data set. The average perpendicular distance compared with manual analysis was 1.1 mm ± 0.3 for CNN3, compared with 1.5 mm ± 1.0 for CNN1 (P < .05) and 1.3 mm ± 0.6 for CNN2 (P < .05). The LV function parameters derived from CNN3 showed a high correlation (r2 ≥ 0.98) and agreement with those obtained by experts for data sets from different vendors and centers. Conclusion A deep learning-based method trained on a data set with high variability can achieve fully automated and accurate cine MRI analysis on multivendor, multicenter cine MRI data. © RSNA, 2018 See also the editorial by Colletti in this issue.

Fully automated analysis of attenuation-corrected SPECT for the long-term prediction of acute myocardial infarction

BackgroundMost prior studies assessing the prognostic value of SPECT myocardial perfusion imaging (MPI) have used semi-quantitative visual analysis. We assessed the feasibility of large-scale fully automated quantitative analysis of SPECT MPI to predict acute myocardial infarction (AMI). Additionally, we examined the impact of attenuation correction (AC) in automated strategies. Methods and Results5960 patients underwent rest/stress SPECT MPI with AC. Left ventricular (LV) segmentation, contour QC check, and quantitation of stress and ischemic total perfusion deficit (sTPD, iTPD) were performed. Only contours flagged for potential errors by QC were visually checked (10%). During long-term follow-up (6.1 ± 2.7 years), 522 patients (9%) had AMI. In Cox models, adjusted for ejection fraction (LVEF) and other relevant covariates, there was a stepwise increase in risk hazard ratios by quartile for sTPD (Q1: 1.00, Q2: 1.26, Q3: 1.66, Q4: 1.79; P < 0.0001) and iTPD (Q1: 1.00, Q2: 1.26, Q3: 1.66, Q4: 1.79; P < 0.0001). Area under curve for AMI prediction by automated measures was similar for AC and non-AC data (sTPD: 0.63 vs 0.64, P = 0.85; iTPD: 0.61 vs 0.61, P = 0.70). Higher AUCs for both AC and non-AC data were seen for AMI occurring in the first 1 year of follow-up (sTPD: 0.71, 0.72; iTPD: 0.70, 0.68). ConclusionsFully automated sTPD was an independent predictor of future AMI events even after adjusting for LVEF and other relevant covariates. AC did not significantly impact predictive accuracy.

Computational Platform Based on Deep Learning for Segmenting Ventricular Endocardium in Long-axis Cardiac MR Imaging.

This paper presents an automated computational platform based on deep learning (DL) approach for left ventricular (LV) and right ventricular (RV) endocardium segmentation in long-axis cine cardiovascular magnetic resonance (CMR). The proposed method uses modified deep U-Net convolutional networks. We trained our model using 4800 images from 40 human subjects (20 healthy volunteers, 20 patients with various cardiac diseases) and validated the technique in 6000 images from 50 subjects (10 healthy volunteers, 40 patients). An average Dice metric of 0.929 ± 0.036 along with an average Jaccard index of 0.869 ± 0.059 were achieved for all the studied subjects. In addition, a high level of correlation and agreement with the ground truth contours for LV ejection fraction (R=0.975), LV fractional area change (R=0.959 to 0.971), and RV fractional area change (R=0.927) were observed. The proposed DL-based segmentation process took less than 3 seconds per subject (or < 30 milliseconds per image over 120 images for each subject). Therefore, our proposed framework offers a promising means to achieve fully automated and rapid segmentation for both LV and RV endocardium in long-axis cine CMR images using an appropriately trained deep convolutional neural network.

Heart Chamber Segmentation from CT Using Convolutional Neural Networks.

CT is routinely used for radiotherapy planning with organs and regions of interest being segmented for diagnostic evaluation and parameter optimization. For cardiac segmentation, many methods have been proposed for left ventricular segmentation, but few for simultaneous segmentation of the entire heart. In this work, we present a convolutional neural networks (CNN)-based cardiac chamber segmentation method for 3D CT with 5 classes: left ventricle, right ventricle, left atrium, right atrium, and background. We achieved an overall accuracy of 87.2% ± 3.3% and an overall chamber accuracy of 85.6 ± 6.1%. The deep learning based segmentation method may provide an automatic tool for cardiac segmentation on CT images.

A Semi-Automated Method for Measurement of Left Ventricular Volumes in 3D Echocardiography

Segmentation of the left ventricle in echocardiography data currently poses a challenge, where delineation of the endocardial borders is a time consuming and difficult task. Though semi-automated and fully automated methods have been developed for left ventricular segmentation, they suffer from a number of drawbacks. These drawbacks include the dependence on large sets of training data and assumptions about the distribution of the intensities of the image. This paper proposes a novel volumetric segmentation algorithm based on an angular slicing approach for 3-D echocardiography scans and a diffeomorphic nonrigid registration method. The proposed method is fast, reproducible, and yields a volumetric segmentation with minimal user interaction. The algorithm was evaluated on 30 participants from the challenge on endocardial 3-D ultrasound segmentation dataset from the medical image computing and computer assisted interventions Challenge 2014. The proposed method yielded the following average distance metrics for the end diastolic volumes: 1) mean absolute distance of 2.36 mm, 2) Hausdorff distance of 8.25 mm, and 3) Dice score of 0.887. For the end systolic volumes, the following average distance metrics were obtained: 1) mean absolute distance of 2.33 mm, 2) Hausdorff distance of 8.95 mm, and 3) Dice score of 0.857. The following clinical metrics for the ejection fraction are reported: 1) modified correlation coefficient of 0.169, 2) bias in mL of −3.96 mL, and 3) standard deviation of 6.85 mL. The results demonstrate the robustness of the proposed volumetric segmentation approach.

Automatic localization of the left ventricular blood pool centroid in short axis cardiac cine MR images.

In this paper, we develop and validate an open source, fully automatic algorithm to localize the left ventricular (LV) blood pool centroid in short axis cardiac cine MR images, enabling follow-on automated LV segmentation algorithms. The algorithm comprises four steps: (i) quantify motion to determine an initial region of interest surrounding the heart, (ii) identify potential 2D objects of interest using an intensity-based segmentation, (iii) assess contraction/expansion, circularity, and proximity to lung tissue to score all objects of interest in terms of their likelihood of constituting part of the LV, and (iv) aggregate the objects into connected groups and construct the final LV blood pool volume and centroid. This algorithm was tested against 1140 datasets from the Kaggle Second Annual Data Science Bowl, as well as 45 datasets from the STACOM 2009 Cardiac MR Left Ventricle Segmentation Challenge. Correct LV localization was confirmed in 97.3% of the datasets. The mean absolute error between the gold standard and localization centroids was 2.8 to 4.7mm, or 12 to 22% of the average endocardial radius. Graphical abstract Fully automated localization of the left ventricular blood pool in short axis cardiac cine MR images.

Fast and Fully Automatic Left Ventricular Segmentation and Tracking in Echocardiography Using Shape-Based B-Spline Explicit Active Surfaces.

Cardiac volume/function assessment remains a critical step in daily cardiology, and 3-D ultrasound plays an increasingly important role. Fully automatic left ventricular segmentation is, however, a challenging task due to the artifacts and low contrast-to-noise ratio of ultrasound imaging. In this paper, a fast and fully automatic framework for the full-cycle endocardial left ventricle segmentation is proposed. This approach couples the advantages of the B-spline explicit active surfaces framework, a purely image information approach, to those of statistical shape models to give prior information about the expected shape for an accurate segmentation. The segmentation is propagated throughout the heart cycle using a localized anatomical affine optical flow. It is shown that this approach not only outperforms other state-of-the-art methods in terms of distance metrics with a mean average distances of 1.81±0.59 and 1.98±0.66 mm at end-diastole and end-systole, respectively, but is computationally efficient (in average 11 s per 4-D image) and fully automatic.

Real-Time X-MRI-Guided Left Ventricular Lead Implantation for Targeted Delivery ofCardiac Resynchronization Therapy.

This study sought to test the feasibility of a purpose-built, integrated software platform to process, analyze, and overlay cardiac magnetic resonance (CMR) data in real time within a combined cardiac catheter laboratory and magnetic resonance imaging scanner suite (X-MRI) to guide left ventricular (LV) lead implantation. Suboptimal LV lead position is a major determinant of poor cardiac resynchronization therapy (CRT) response, and the optimal site is highly patient specific. Pacing myocardial scar is associated with poorer outcomes; conversely, targeting latest mechanical activation (LMA) may improve them. Fourteen patients (age 74 ± 5.1 years; New York Heart Association functional class: 2.7 ± 0.4; 86% ischemic with ejection fraction 27 ± 7.6%; QRSd: 157 ± 19 ms) underwent CMR followed by immediate CRT implantation using derived scar and dyssynchrony data, overlaid onto fluoroscopy in an X-MRI suite. Rapid LV segmentation enabled detailed scar quantification, identification of LMA segments, and selection of myocardial targets. At coronary venography, the CMR-derived 3-dimensional shell was fused, enabling identification of viable venous targets subtended by target segments for LV lead placement. The platform was successful in all 14 patients, of whom 10 (71%) were paced in pre-procedurally defined target segments. Pacing in CMR-defined target segments (out of scar) showed a significant decrease in the LV capture threshold (mean difference: 2.4 [1.5 to 3.2]; p< 0.001) and shorter paced QRS duration (mean difference: 25 [15 to 34]; p< 0.001) compared with pacing in areas of CMR determined scar. In 5 (36%) patients with extensive scar in the posterolateral wall, CMR guidance enabled successful lead delivery in an alternative anatomically favorable site. Radiation dose and implant times were similar to historical controls (p= NS). Real-time CMR-guided LV lead placement is feasible and achievable in a single clinical setting andmayprove helpful to preferentially select sites for LV lead placement.

Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences.

Automated left ventricular (LV) segmentation is crucial for efficient quantification of cardiac function and morphology to aid subsequent management of cardiac pathologies. In this paper, we parameterize the complete (all short axis slices and phases) LV segmentation task in terms of the radial distances between the LV centerpoint and the endo- and epicardial contours in polar space. We then utilize convolutional neural network regression to infer these parameters. Utilizing parameter regression, as opposed to conventional pixel classification, allows the network to inherently reflect domain-specific physical constraints. We have benchmarked our approach primarily against the publicly-available left ventricle segmentation challenge (LVSC) dataset, which consists of 100 training and 100 validation cardiac MRI cases representing a heterogeneous mix of cardiac pathologies and imaging parameters across multiple centers. Our approach attained a .77 Jaccard index, which is the highest published overall result in comparison to other automated algorithms. To test general applicability, we also evaluated against the Kaggle Second Annual Data Science Bowl, where the evaluation metric was the indirect clinical measures of LV volume rather than direct myocardial contours. Our approach attained a Continuous Ranked Probability Score (CRPS) of .0124, which would have ranked tenth in the original challenge. With this we demonstrate the effectiveness of convolutional neural network regression paired with domain-specific features in clinical segmentation.

Improving left ventricular segmentation in four-dimensional flow MRI using intramodality image registration for cardiac blood flow analysis.

Assessment of blood flow in the left ventricle using four-dimensional flow MRI requires accurate left ventricle segmentation that is often hampered by the low contrast between blood and the myocardium. The purpose of this work is to improve left-ventricular segmentation in four-dimensional flow MRI for reliable blood flow analysis. The left ventricle segmentations are first obtained using morphological cine-MRI with better in-plane resolution and contrast, and then aligned to four-dimensional flow MRI data. This alignment is, however, not trivial due to inter-slice misalignment errors caused by patient motion and respiratory drift during breath-hold based cine-MRI acquisition. A robust image registration based framework is proposed to mitigate such errors automatically. Data from 20 subjects, including healthy volunteers and patients, was used to evaluate its geometric accuracy and impact on blood flow analysis. High spatial correspondence was observed between manually and automatically aligned segmentations, and the improvements in alignment compared to uncorrected segmentations were significant (P < 0.01). Blood flow analysis from manual and automatically corrected segmentations did not differ significantly (P > 0.05). Our results demonstrate the efficacy of the proposed approach in improving left-ventricular segmentation in four-dimensional flow MRI, and its potential for reliable blood flow analysis. Magn Reson Med 79:554-560, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Cardiac PET/CT with Rb-82: optimization of image acquisition and reconstruction parameters

BackgroundOur aim was to characterize the influence of time-of-flight (TOF) and point spread function (PSF) recovery corrections, as well as ordered subset expectation maximization (OSEM) reconstruction parameters, in 82Rb PET/CT quantification of myocardial blood flow (MBF) and myocardial flow reserve (MFR).Rest and stress list-mode dynamic 82Rb PET acquisition data from 10 patients without myocardial flow defects and 10 patients with myocardial blood flow defects were reconstructed retrospectively. OSEM reconstructions were performed with Gaussian filters of 4, 6, and 8 mm, different iterations, and subset numbers (2 × 24; 2 × 16; 3 × 16; 4 × 16). Rest and stress global, regional, and segmental MBF and MFR were computed from time activity curves with FlowQuant© software. Left ventricular segmentation using the 17-segment American Heart Association model was obtained.ResultsWhole left ventricle (LV) MBF at rest and stress were 0.97 ± 0.30 and 2.30 ± 1.00 mL/min/g, respectively, and MFR was 2.40 ± 1.13. Concordance was excellent and all reconstruction parameters had no significant impact on MBF, except for the exclusion of TOF which led to significantly decreased concordance in rest and stress MBF in patients with or without perfusion defects on a coronary artery basis and in MFR in patients with perfusion defects.ConclusionsChanges in reconstruction parameters in perfusion 82Rb PET/CT studies influence quantitative MBF analysis. The inclusion of TOF information in the tomographic reconstructions had significant impact in MBF quantification.

Imagej's Contribution to Left Ventricular Segmentation in Myocardial Perfusion Imaging

Imagej's Contribution to Left Ventricular Segmentation in Myocardial Perfusion Imaging

Quantification of mitral regurgitation in patients with hypertrophic cardiomyopathy using aortic and pulmonary flow data: impacts of left ventricular outflow tract obstruction and different left ventricular segmentation methods

BackgroundCardiovascular magnetic resonance (CMR) imaging in patients with hypertrophic cardiomyopathy (HCM) enables the assessment of not only left ventricular (LV) hypertrophy and scarring but also the severity of mitral regurgitation. CMR assessment of mitral regurgitation is primarily based on the difference between LV stroke volume (LVSV) and aortic forward flow (Ao) measured using the phase-contrast (PC) technique. However, LV outflow tract (LVOT) obstruction causing turbulent, non-laminar flow in the ascending aorta may impact the accuracy of aortic flow quantification, leading to false conclusions regarding mitral regurgitation severity. Thus, we decided to quantify mitral regurgitation in patients with HCM using Ao or, alternatively, main pulmonary artery forward flow (MPA) for mitral regurgitation volume (MRvol) calculations.MethodsThe analysis included 143 prospectively recruited subjects with HCM and 15 controls. MRvol was calculated as the difference between LVSV computed with either the inclusion (LVSVincl) or exclusion (LVSVexcl) of papillary muscles and trabeculations from the blood pool and either Ao (MRvolAoi or MRvolAoe) or MPA (MRvolMPAi or MRvolMPAe). The presence or absence of LVOT obstruction was determined based on Doppler echocardiography findings.ResultsMRvolAoi was higher than MRvolMPAi in HCM patients with LVOT obstruction [47.0 ml, interquartile range (IQR) = 31.5–60.0 vs. 35.5 ml, IQR = 26.0–51.0; p < 0.0001] but not in non-obstructive HCM patients (23.0 ml, IQR = 16.0–32.0 vs. 24.0 ml, IQR = 15.3–32.0; p = 0.26) or controls (18.0 ml, IQR = 14.3–21.8 vs. 20.0 ml, IQR = 14.3–22.0; p = 0.89). In contrast to controls and HCM patients without LVOT obstruction, in HCM patients with LVOT obstruction, aortic flow-based MRvol (MRvolAoi) was higher than pulmonary-based findings (MRvolMPAi) (bias = 9.5 ml; limits of agreement: −11.7–30.7 with a difference of 47 ml in the extreme case). The differences between aortic-based and pulmonary-based MRvol values calculated using LVSVexcl mirrored those derived using LVSVincl. However, MRvol values calculated using LVSVexcl were lower in all the groups analyzed (HCM with LVOT obstruction, HCM without LVOT obstruction, and controls) and with all methods of MRvol quantification used (p ≤ 0.0001 for all comparisons).ConclusionsIn HCM patients, LVOT obstruction significantly affects the estimation of aortic flow, leading to its underestimation and, consequently, to higher MRvol values than those obtained with MPA-based MRvol calculations.

“Same-Patient Processing” for multiple cardiac SPECT studies. 1. Improving LV segmentation accuracy

ObjectivesThis paper describes a novel approach (same-patient processing, or SPP) aimed at improving left ventricular segmentation accuracy in patients with multiple SPECT studies, and evaluates its performance compared to conventional processing in a large population of 962 patients undergoing rest and stress electrocardiography-gated SPECT MPI, for a total of 5,772 image datasets (6 per patient). MethodsEach dataset was independently processed using a standard algorithm, and a shape quality control score (SQC) was produced for every segmentation. Datasets with a SQC score higher than a specific threshold, suggesting algorithmic failure, were automatically reprocessed with the SPP-modified algorithm, which incorporates knowledge of the segmentation mask location in the other datasets belonging to the same patient. Experienced operators blinded as to whether datasets had been processed based on the standard or SPP approach assessed segmentation success/failure for each dataset. ResultsThe SPP approach reduced segmentation failures from 219/5772 (3.8%) to 42/5772 (0.7%) overall, with particular improvements in attenuation corrected (AC) datasets with high extra-cardiac activity (from 100/962 (10.4%) to 12/962 (1.4%) for rest AC, and from 41/962 (4.3%) to 9/962 (0.9%) for stress AC). The number of patients who had at least one of their 6 datasets affected by segmentation failure decreased from 141/962 (14.7%) to 14/962 (1.7%) using the SPP approach. ConclusionWhenever multiple image datasets for the same patient exist and need to be processed, it is possible to deal with the images as a group rather than individually. The same-patient processing approach can be implemented automatically, and may substantially reduce the need for manual reprocessing due to cardiac segmentation failure.

Detection and diagnosis of dilated cardiomyopathy and hypertrophic cardiomyopathy using image processing techniques

Major heart diseases like heart muscle damage and valvular problems are diagnosed using echocardiogram. Since the echocardiogram is an image or sequence of images with less information the cardiologist spends more time to predict or to make decision. Automating the detection and diagnosis of dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) is a key enabling technology in computer aided diagnosis systems. In this paper, a system is proposed to automatically detect and diagnose dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM). This system performs denoising, enhancement, before left ventricular segmentation is carried out in the individual frames. Using the segmented left ventricle, the LV parameters like volume and ejection fraction (EF) are calculated and also the end-diastolic LV is extracted. The PCA and DCT features are obtained from the extracted end-diastolic LV and the classifiers BPNN, SVM and combined K-NN are used to classify the normal hearts, hearts affected with DCM and hearts affected with HCM. The PCA feature with BPNN classifier gives a highest overall accuracy of 92.04% in classifying normal and abnormal hearts. Experiments over 60 echocardiogram videos expose that the proposed system can be effectively utilized to detect and diagnose DCM and HCM.

Simultaneous extraction of endocardial and epicardial contours of the left ventricle by distance regularized level sets.

Segmentation of the cardiac left ventricle (LV) is still an open problem and is challenging due to the poor contrast between tissues around the epicardium and image artifacts. To extract the endocardium and epicardium of the cardiac left ventricle accurately, the authors propose a two-layer level set approach for segmentation of the LV from cardiac magnetic resonance short-axis images. In the proposed method, the endocardium and epicardium are represented by two specified level contours of a level set function. Segmentation of the LV is formulated as a problem of optimizing the level set function such that these two level contours best fit the epicardium and endocardium, subject to a distance regularization (DR) term to preserve a smoothly varying distance between them. The DR term introduces a desirable interaction between the two level contours of a single level set function, which contributes to preserve the anatomical geometry of the epicardium and endocardium of the LV. In addition, the proposed method has an intrinsic ability to deal with intensity inhomogeneity in MR images, which is a common image artifact in MRI. Their method is quantitatively validated by experiments on the datasets for the MICCAI 2009 grand challenge on left ventricular segmentation and the MICCAI 2013 challenge workshop on segmentation: algorithms, theory and applications (SATA). To overcome discontinuity of 2D segmentation results at some adjacent slices for a few cases, the authors extend distance regularized two-layer level set to 3D to refine the segmentation results. The corresponding metrics for their method are better than the methods in the MICCAI 2009 challenge. Their method was ranked at the first place in terms of Hausdorff distance and the second place in terms of Dice similarity coefficient in the MICCAI 2013 challenge. Experimental results demonstrate the advantages of their method in terms of segmentation accuracy and consistency with the heart anatomy.

Reproducibility and observer variability of tissue phase mapping for the quantification of regional myocardial velocities.

To systematically investigate the reproducibility of global and segmental left ventricular (LV) velocities derived from tissue phase mapping (TPM). Breath held and ECG synchronized TPM data (spatial/temporal resolution = 2 × 2mm(2)/20.8ms) were acquired in 18 healthy volunteers. To analyze scan-rescan variability, TPM was repeated in all subjects during a second visit separated by 16 ± 5 days. Data analysis included LV segmentation, and quantification of global and regional (AHA 16-segment modal) metrics of LV function [velocity-time curves, systolic and diastolic peak and time-to-peak (TTP) velocities] for radial (Vr), long-axis (Vz) and circumferential (VΦ) LV velocities. Mean velocity time curves in basal, mid-ventricular, and apical locations showed highly similar LV motion patterns for all three velocity components (Vr, VΦ, Vz) for scan and rescan. No significant differences for both systolic and diastolic peak and TTP myocardial velocities were observed. Segmental analysis revealed similar regional peak Vr and Vz during both systole and diastole except for three LV segments (p = 0.045, p = 0.033, and p = 0.009). Excellent (p < 0.001) correlations between scans and rescan for peak Vr (R(2) = 0.92), peak Vz (R(2) = 0.90), radial TTP (R(2) = 0.91) and long-axis TTP (R(2) = 0.88) confirmed good agreement. Bland-Altman analysis demonstrated excellent intra-observer and good inter-observer analysis agreement but increased variability for long axis peak velocities. TPM based analysis of global and regional myocardial velocities can be performed with good reproducibility. Robustness of regional quantification of long-axis velocities was limited but spatial velocity distributions across the LV could reliably be replicated.

Histopathological Correlates of Global and Segmental Left Ventricular Systolic Dysfunction in Experimental Chronic Chagas Cardiomyopathy.

BackgroundChronic Chagas cardiomyopathy in humans is characterized by segmental left ventricular wall motion abnormalities (WMA), mainly in the early stages of disease. This study aimed at investigating the detection of WMA and its correlation with the underlying histopathological changes in a chronic Chagas cardiomyopathy model in hamsters.Methods and ResultsFemale Syrian hamsters (n=34) infected with 3.5×104 or 105 blood trypomastigote Trypanosoma cruzi (Y strain) forms and an uninfected control group (n=7) were investigated. After 6 or 10 months after the infection, the animals were submitted to in vivo evaluation of global and segmental left ventricular systolic function by echocardiography, followed by euthanasia and histological analysis for quantitative assessment of fibrosis and inflammation with tissue sampling in locations coinciding with the left ventricular wall segmentation employed at the in vivo echocardiographic evaluation. Ten of the 34 infected animals (29%) showed reduced left ventricular ejection fraction (<73%). Left ventricular ejection fraction was more negatively correlated with the intensity of inflammation (r=−0.63; P<0.0001) than with the extent of fibrosis (r=−0.36; P=0.036). Among the 24 animals with preserved left ventricular ejection fraction (82.9±5.5%), 8 (33%) showed segmental WMA predominating in the apical, inferior, and posterolateral segments. The segments exhibiting WMA, in comparison to those with normal wall motion, showed a greater extent of fibrosis (9.3±5.7% and 7±6.3%, P<0.0001) and an even greater intensity of inflammation (218.0±111.6 and 124.5±84.8 nuclei/mm², P<0.0001).ConclusionsIsolated WMA with preserved global systolic left ventricular function is frequently found in Syrian hamsters with experimental chronic Chagas cardiomyopathy whose underlying histopathological features are mainly inflammatory.